This paper proposes a new approach to produce classification rules based on evolutionary computation with novel crossover and mutation operators customized for execution on graphics processing unit (GPU). Also, a novel method is presented to define the fitness function, i.e. the function which measures quantitatively the accuracy of the rule. The proposed fitness function is benefited from parallelism due to the parallel execution of data instances. To this end, two novel concepts; coverage matrix and reduction vectors are used and an altered form of the reduction vector is compared with previous works. Our CUDA program performs operations on coverage matrix and reduction vector in parallel. Also these data structures are used for evaluation of fitness function and calculation of genetic operators in parallel. We proposed a vector called average coverage to handle crossover and mutation properly. Our proposed method obtained a maximum accuracy of 99.74% for Hepatitis C Virus (HCV) dataset, 95.73% for Poker dataset, and 100% for COVID-19 dataset. Our speedup is higher than 20% for HCV and COVID-19, and 50% for Poker, compared to using single core processors.

本文提出了一种基于进化计算生成分类规则的新方法，该方法具有为在图形处理单元 (GPU) 上执行而定制的新颖交叉和变异算子。此外，还提出了一种定义适应度函数的新方法，即定量测量规则准确性的函数。由于数据实例的并行执行，建议的适应度函数受益于并行性。为此，提出了两个新颖的概念；使用覆盖矩阵和归约向量，并将改变形式的归约向量与以前的工作进行比较。我们的 CUDA 程序并行执行覆盖矩阵和缩减向量的操作。这些数据结构也用于并行评估适应度函数和计算遗传算子。我们提出了一个称为平均覆盖率的向量来正确处理交叉和变异。我们提出的方法在丙型肝炎病毒 (HCV) 数据集上获得了 99.74% 的最大准确率，在 Poker 数据集上获得了 95.73% 的最大准确率，在 COVID-19 数据集上获得了 100% 的最大准确率。与使用单核处理器相比，我们的 HCV 和 COVID-19 加速比高出 20%，扑克提高 50%。